scine-chemoton 4.0.0

Software driving the automated exploration of chemical reaction networks

Introduction

With Chemoton you can explore complex chemical reaction networks in an automated fashion. Based on a Python framework, workflows can be built that probe reactivity of chemical systems with quantum chemical methods. Various quantum chemical software programs and job schedulers are supported by the back-end software SCINE Puffin.

License and Copyright Information

Chemoton is distributed under the BSD 3-clause “New” or “Revised” License. For more license and copyright information, see the file LICENSE.txt in the repository.

Installation

Prerequisites

The key requirements for Chemoton are the Python packages scine_utilities, scine_database, and scine_molassember. These packages are available from PyPI and can be installed using pip. However, these packages can also be compiled from sources. For the latter case please visit the repositories of each of the packages and follow their guidelines or bootstrap a puffin which will install the same dependencies.

Installation

Chemoton can be installed using pip (pip3) once the repository has been cloned:

git clone https://github.com/qcscine/chemoton.git
cd chemoton
pip install -r requirements.txt
pip install .

A non-root user can install the package using a virtual environment, or the --user flag.

The documentation can be found online, or it can be built using:

make -C docs html

It is then available at:

<browser-name> docs/build/html/index.html

In order to build the documentation, you need a few extra Python packages, which are not installed automatically together with Chemoton. In order to install them, run

pip install -r requirements-dev.txt

Tutorial

Minimal Example

Assuming that Chemoton has successfully been installed, a small example exploration can be started by running Chemoton’s main function. It requires a database running on localhost listening to the default mongodb port 27017; additionally a puffin instance has to be running and checking the database named default.

Setting up these things may look somewhat like this:

1. Start a mongodb server. Limit its memory usage and maybe customize where to log and store the data.

mongod --fork --port=27017 -dbpath=<path to db storage dir> -wiredTigerCacheSizeGB=1 --logpath=mongo.log

2. Configure and bootstrap a puffin:

pip install scine-puffin
python3 -m scine_puffin configure
# Edit the generated puffin.yaml here
python3 -m scine_puffin -c puffin.yaml bootstrap

3. Source the puffin settings and tell it to listen to the correct DB. (Hostname and port should be the default ones.) Then start it.

source puffin.sh
export PUFFIN_DATABASE_NAME=default
python3 -m scine_puffin -c puffin.yaml start

4. Run the Chemoton exploration defined in the __main__ function:

python3 -m scine_chemoton wipe

The optional wipe argument will start the example exploration with a clean default DB; giving the continue argument will reuse old data.

Expanding on the Minimal Example

The functionalities used in Chemoton’s __main__.py are a good starting point for most simple explorations. The file contains a lot of settings that are explicitly set to their defaults in order to show their existence.

While we recommend to read the documentation of Chemoton, tinkering with explorations can be as simple as:

chemoton_main=$(python3 -c 'from scine_chemoton import __main__ as m; print(m.__file__)')
echo $chemoton_main
cp $chemoton_main my_awesome_exploration.py

and editing the file to your liking: disabling gears, adding filters or just changing methods.

In order to directly have analysis tools for the network at hand or run explorations without coding, we recommend our graphical user interface Heron.

How to Cite

When publishing results obtained with Chemoton, please cite the corresponding release as archived on Zenodo (DOI 10.5281/zenodo.6695583; please use the DOI of the respective release).

In addition, we kindly request you to cite the following article when using Chemoton:

J. P. Unsleber, S. A. Grimmel, M. Reiher, “Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks”, J. Chem. Theory Comput., 2022, 18, 5393.

If you are applying SCINE Pathfinder in your exploration or analysis, we kindly request you to cite the following article:

P. L. Türtscher, M. Reiher, “Pathfinder - Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-Based Approach”, J. Chem. Inf. Model., 2023, 63, 147.

If you are applying kinetic modeling in your exploration or analysis, we kindly request you to cite the following article: J. Proppe, M. Reiher, “Mechanism Deduction from Noisy Chemical Reaction Networks”, J. Chem. Theory Comput., 2019, 15, 357.

M. Bensberg, M. Reiher, “Concentration-Flux-Steered Mechanism Exploration with an Organocatalysis Application”, Isr. J. Chem., 2023, 63, 147.

If you are applying the Steering Wheel in your exploration, we kindly request you to cite the following article: M. Steiner, M. Reiher, “A human-machine interface for automatic exploration of chemical reaction networks”, Nat. Commun., 2024, 15, 3680.

Furthermore, when publishing results obtained with any SCINE module, please cite the following paper:

T. Weymuth, J. P. Unsleber, P. L. Türtscher, M. Steiner, J.-G. Sobez, C. H. Müller, M. Mörchen, V. Klasovita, S. A. Grimmel, M. Eckhoff, K.-S. Csizi, F. Bosia, M. Bensberg, M. Reiher, “SCINE—Software for chemical interaction networks”, J. Chem. Phys., 2024, 160, 222501 (DOI 10.1063/5.0206974).

Support and Contact

In case you should encounter problems or bugs, please write a short message to [e-mail].